In mathematics and digital signal processing, quantization is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite …

Nov 6, 2025 · Quantization is a model optimization technique that reduces the precision of numerical values such as weights and activations in models to make them faster and more efficient. It helps …

Quantization is the process of mapping continuous infinite values to a smaller set of discrete finite values. In the context of simulation and embedded computing, it is about approximating real-world …

What is quantization in machine learning? Quantization is a technique for lightening the load of executing machine learning and artificial intelligence (AI) models. It aims to reduce the memory …

Jul 20, 2025 · Among many optimization techniques to improve AI inference performance, quantization has become an essential method when deploying modern AI models into real-world services.

Source coding for analog values is usually called quantization. Note that this is also the middle layer for waveform encoding/decoding. Figure 3.1: Encoding and decoding of discrete sources, analog …

Quantization is representing the sampled values of the amplitude by a finite set of levels, which means converting a continuous-amplitude sample into a discrete-time signal.

Oct 16, 2025 · Quantization is a data compression technique that allows machine learning models to operate more efficiently without substantial performance loss.

Quantization is a process of converting a continuous set of values (like all the possible real - number values) into a discrete set. In simple terms, it's like taking a wide range of possibilities and breaking …

6 days ago · This was the first quantization. When that ran into trouble at relativistic energies, the field was quantized, the "second quantization". Second quantization is the conceptual leap that turns our …